Abstract 3362: Multiscale EGFR mutated NSCLC tumor heterogeneity knowledge-based model predicts tumor growth under gefitinib: An avenue to <i>in silico</i> clinical trial
Authors: Michael Duruisseaux, Adèle L'Hostis, Emmanuel Peyronnet, Evgueni Jacob, Ben M.W. Illigens, Jim Bosley, Riad Kahoul, Jean-Louis Palgen, Claudio Monteiro
Journal: Cancer Research (2022)

Abstract Introduction: Non-small cell lung cancer (NSCLC) response to gefitinib depends on epidermal growth factor receptor (EGFR) mutational status. Response differs according to tumor heterogeneity, notably through clonal selection according to genetic background. We developed an in silico EGFR mutated lung adenocarcinoma (EGFR+ LUAD) model topredict the effect of EGFR mutations on tumor size in advanced LUAD patients using a mechanistic representation of tumor progression, including response to gefitinib. Tumor heterogeneity, age, gender, initial clinical stage, and smoking status are included as covariates. Methods: 5-step in silico model development: (i) Model Building: Biology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from literature and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR downstream-pathways, tumor growth and heterogeneity, gefitinib-PK/PD, treatment-induced resistance and clinical outcome were modeled in a computational simulation with 43 variables, 170 parameters and 18 to 83 ODEs reflecting intra-tumor heterogeneity. (ii) Calibration: Published spheroid, xenograft and clinical data were used for stepwise calibration. (iii) Virtual populations (VPOP): VPOPs were generated for validation and benchmarking respectively, adapting baseline characteristics of a real population.(iv) Validation: A VPOP with comparable baseline characteristics was tested against published patient data.(v) Benchmarking against a Bayesian reference model : (1) coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, (2) coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Results: Our model computed in silico data comparable to the reference model without use of original data for calibration (Figure 1B.2: experimental vs. simulated, precision of 68%, overlap of 91%). The reference model reported precision of 72% and overlap of 86%. Therefore, the ISELA model has a better percentage of the literature data area contained in the simulated one while the Bayesian model presents a better percentage of the simulated data contained in the literature one. Conclusion: We simulated tumor growth and treatment response in advanced EGFR+ LUAD patients and successfully validated results with published data, and compared it to an already published model with the same context of use. Both models successfully provide a reliable description of longitudinal tumor size when compared to each other or to observed data. Our model provides a benchmark for future in silico clinical trials. Citation Format: Michael Duruisseaux, Adèle L'Hostis, Emmanuel Peyronnet, Evgueni Jacob, Ben M.W. Illigens, Jim Bosley, Riad Kahoul, Jean-Louis Palgen, Claudio Monteiro. Multiscale EGFR mutated NSCLC tumor heterogeneity knowledge-based model predicts tumor growth under gefitinib: An avenue to in silico clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3362.